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Aim Understanding temporal changes in aquatic communities is essential to address the freshwater biodiversity crisis. In particular, it is important to understand the patterns and drivers of spatial variation in local community dynamics, generalizing temporal trends from discrete locations to entire landscapes that are the main focus of management. Here, we present a framework for producing spatially continuous views of community dynamics, focusing on stream fish affected by hydropower development. Location River Sabor, NE Portugal. Methods We sampled stream fish at thirty sites between 2012 and 2019. Community trajectory analysis was used to quantify the directionality and velocity of community change, and the geometric resemblance of community trajectories between sites. Geostatistical models for stream networks were used to relate metrics describing community dynamics to environmental variables, while controlling for Euclidean and hydrologic spatial dependencies, and to map spatial variation in community dynamics across the watershed. Results Trajectories in multivariate space underlined strong temporal dynamics, with local communities deviating and returning to previous states, but without evidence for directional changes. Accordingly, directionality values were low and not consistently affected by environmental variables. The velocity of community change varied markedly across the watershed and it was strongly affected by stream order and elevation, with faster changes observed in lowland streams draining into hydroelectric reservoirs and with a high proportion of exotic species. Pairwise distances between community trajectories were strongly related to hydrologic and environmental distances between sites. Main conclusions Local stream fish communities were in a loose equilibrium across the watershed, but they fluctuated at a faster rate closer to a hydroelectric reservoir. Integrating community trajectory analysis and geostatistical modelling provides a relatively simple framework to understand how, where and why temporal community dynamics vary across dendritic stream networks and to visualize spatial patterns of community change over time in relation to anthropogenic impacts.

Human mobility datasets, such as traffic flow data, reveal the connections between urban spaces. A novel framework is proposed to explore the spatial association between urban commercial and residential spaces via consumption travel flows in Shanghai. A social network analysis and a community detection method are employed using taxi trajectory data during the daytime to validate the framework. The machine learning-based approach, such as the community detection method, can overcome the limitation regarding spatial uncertainty and spatial effects. The empirical findings suggest that people's commercial activities are sensitive to the power of accessible commercial centers and travel distances. The high-level commercial centers would contribute to the monocentric structure in the outer urban region based on consumption flows. In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people's mobility based on the trajectory big data, which is promising in future urban research.

Human mobility datasets, such as traffic flow data, reveal the connections between urban spaces. A novel framework is proposed to explore the spatial association between urban commercial and residential spaces via consumption travel flows in Shanghai. A social network analysis and a community detection method are employed using taxi trajectory data during the daytime to validate the framework. The machine learning-based approach, such as the community detection method, can overcome the limitation regarding spatial uncertainty and spatial effects. The empirical findings suggest that people’s commercial activities are sensitive to the power of accessible commercial centers and travel distances. The high-level commercial centers would contribute to the monocentric structure in the outer urban region based on consumption flows. In the central urban region, increasing the number of high-level commercial centers and making the powers of commercial centers hierarchical can contribute to a polycentric mobility pattern of people's consumption. This research contributes to the literature by providing a novel framework to model, analyze and visualize people’s mobility based on the trajectory big data, which is promising in future urban research.

Ecologists have long been interested in how communities change over time. Addressing questions about community dynamics requires ways of representing and comparing the variety of dynamics observed across space. Until now, most analytical frameworks have been based on the comparison of synchronous observations across sites and between repeated surveys. An alternative perspective considers community dynamics as trajectories in a chosen space of community resemblance and utilizes trajectories as objects to be analyzed and compared using their geometry. While methods that take this second perspective exist, for example to test for particular trajectory shapes, there is a need for formal analytical frameworks that fully develop the potential of this approach. By adapting concepts and procedures used for the analysis of spatial trajectories, we present a framework for describing and comparing community trajectories. A key element of our contribution is the means to assess the geometric resemblance between trajectories, which allows users to describe, quantify, and analyze variation in community dynamics. We illustrate the behavior of our framework using simulated data and two spatiotemporal community data sets differing in the community properties of interest (species composition vs. size distribution of individuals). We conclude by evaluating the advantages and limitations of our community trajectory analysis framework, highlighting its broad domain of application and anticipating potential extensions.

Social isolation and fragmentation represent global challenges, with the former stemming from a lack of interaction and the latter from exclusive mobs—both rooted in communication issues. Addressing these challenges, the philosophical realm introduces the concept of the “mixbiotic society.” In this framework, individuals with diverse freedoms and values mix together in physical proximity with diverse mingling, recognizing their respective “fundamental incapacities” and uniting in solidarity. This study aims to provide novel measures to balance freedom and solidarity, specifically the intermediate between isolation and mobbing, within a mixbiotic society. To achieve this, we introduce simplified measures to evaluate dynamic communication patterns. These measures complement traditional social network analysis of static structures and conventional entropy-based assessments of dynamic patterns. Our specific hypothesis posits that the measures corresponding to four distinct phases are established by representing communication patterns as multidimensional vectors. These measures include the mean of Euclidean distance to quantify “mobism” for fragmentation, the relative distance change for “atomism” indicating isolation, and a composite measure derived from multiplying the mean and variance of cosine similarity for “mixism,” reflecting the well-going state of a mixbiotic society. Additionally, nearly negligible measures correspond to “nihilism.” Through the evaluation of seven real-society datasets (high school, primary school, workplace, village, conference, online community, and email), we demonstrate the utility of the “mixism” measure in assessing freedom and solidarity in society. These measures can be employed to typify communities on a radar chart and a communication trajectory graph. The superiority of the measures lies in their ability to evaluate dynamic patterns, ease of calculation, and easily interpretable meanings compared to conventional analyses. As a future development, alongside additional validation using diverse datasets, the mixbiotic society measures will be employed to analyze social issues and applied in the fields of digital democracy and platform cooperativism.

Anthropogenic activities are changing the state of ecosystems worldwide, affecting community composition and often resulting in loss of biodiversity. Rivers are among the most impacted ecosystems. Recording their current state with regular biomonitoring is important to assess the future trajectory of biodiversity. Traditional monitoring methods for ecological assessments are costly and time-intensive. Here, we compared monitoring of macroinvertebrates based on environmental DNA (eDNA) sampling with monitoring based on traditional kick-net sampling to assess biodiversity patterns at 92 river sites covering all major Swiss river catchments. From the kick-net community data, a biotic index (IBCH) based on 145 indicator taxa had been established. The index was matched by the taxonomically annotated eDNA data by using a machine learning approach. Our comparison of diversity patterns only uses the zero-radius Operational Taxonomic Units assigned to the indicator taxa. Overall, we found a strong congruence between both methods for the assessment of the total indicator community composition (gamma diversity). However, when assessing biodiversity at the site level (alpha diversity), the methods were less consistent and gave complementary data on composition. Specifically, environmental DNA retrieved significantly fewer indicator taxa per site than the kick-net approach. Importantly, however, the subsequent ecological classification of rivers based on the detected indicators resulted in similar biotic index scores for the kick-net and the eDNA data that was classified using a random forest approach. The majority of the predictions (72%) from the random forest classification resulted in the same river status categories as the kick-net approach. Thus, environmental DNA validly detected indicator communities and, combined with machine learning, provided reliable classifications of the ecological state of rivers. Overall, while environmental DNA gives complementary data on the macroinvertebrate community composition compared to the kick-net approach, the subsequently calculated indices for the ecological classification of river sites are nevertheless directly comparable and consistent.

Long‐term monitoring programs are fundamental to detect changes in ecosystem health and understand ecological processes. In the current context of increasing anthropogenic threats on marine ecosystems, understanding the dynamics and response of communities becomes essential. We used data collected over 14 years in the REBENT benthic coastal invertebrates monitoring program, at a regional scale in the North‐East Atlantic, covering a total of 26 sites and 979 taxa. Four distinct habitats were studied: two biogenic habitats associated with foundation species in the intertidal and subtidal zones and two bare sedimentary habitats in the same respective tidal zones. We used community trajectory analysis (CTA), a statistical approach that allows for quantitative measures and comparisons of temporal trajectories of ecosystems. We compared observed community trajectories to trajectories simulated under a non‐directional null model in order to better understand the dynamics of the communities, their potential drivers, and the role of the studied habitats in these dynamics. Despite strong differences in the community compositions between sites and habitats, the communities followed non‐directional dynamics during the 14 years monitored, which suggested stability at the regional scale. However, the shape, size, and direction of the trajectories of benthic communities were more similar within than among habitats, also suggesting the influence of the nature of the habitat on community dynamics. Results showed a higher variability in community composition the first years of the monitoring in the intertidal bare habitat and confirmed the role of biogenic habitats in maintaining temporal stability. They also highlighted the need to apprehend the role of transient and rare species and the scale of observation in temporal beta diversity analyses. Finally, our study confirmed the usefulness of CTA to link observed trajectory patterns to fundamental ecological processes.

The underrepresentation of girls from nondominant backgrounds in the sciences and engineering continues despite recent gains in achievement. This longitudinal ethnographic study traces the identity work that girls from non-dominant backgrounds do as they engage in science-related activities across school, club, and home during the middle school years. Building a conceptual argument for identity trajectories, the authors discuss the ongoing, cumulative, and contentious nature of identity work and the mechanisms that foster critical shifts in trajectories. The authors argue that the girls view possible future selves in science when their identity work is recognized, supported, and leveraged toward expanded opportunities for engagement in science. This process yields layered meanings of (possible) selves and of science and reconfigures meaningful participation in science.

Aquatic decomposition, as a forensic discipline, has been largely under-investigated as a consequence of the highly complex and influential variability of the water environment. The limitation to the adaptability of scenario specific results justifies the necessity for experimental research to increase our understanding of the aquatic environment and the development of post-mortem submersion interval (PMSI) methods of estimation. This preliminary research aims to address this contextual gap by assessing the variation in the bacterial composition of aquatic biofilms as explained by water parameter measurements over time, associated with clothed and bare decomposing remains. As part of three field investigations, a total of 9 still-born piglets (n = 3, per trial) were used as human analogues and were submerged bare or clothed in either natural cotton or synthetic nylon. Changes in the bacterial community composition of the water surrounding the submerged remains were assessed at 4 discrete time points post submersion (7, 14, 21 and 28 days) by 16 S rRNA gene Next Generation Sequencing analysis and compared to coinciding water parameter measurements (i.e. conductivity, total dissolved solids (TDS), salinity, pH, and dissolved oxygen (DO)). Bacterial diversity was found to change over time and relative to clothing type, where significant variation was observed between synthetic nylon samples and bare/cotton samples. Seasonality was a major driver of bacterial diversity, where substantial variation was found between samples collected in early winter to those collected in mid - late winter. Water parameter measures of pH, salinity and DO were identified to best explain the global bacterial community composition and their corresponding dynamic trajectory patterns overtime. Further investigation into bacterial community dynamics in accordance with varying environmental conditions could potentially lead to the determination of influential extrinsic factors that may drive bacterial activity in aquatic decomposition. Together with the identification of potential bacterial markers that complement the different stages of decomposition, this may provide a future approach to PMSI estimations. •Clothing and clothing type have considerable impact on the global variation of decomposer aquatic bacterial communities.•Environmental variables including salinity, dissolved oxygen and pH drive bacterial assemblages of submerged remains.•Similar trends in Proteobacteria: Bacteroidota ratios indicate this could be used as a biomarker for estimating PMSI.•Differences in daily temperature highs and lows may have influence on the diversity of aquatic bacterial communities.

Ecologists working with stable isotopes have to deal with complex datasets including temporal and spatial replication, which makes the analysis and the representation of patterns of change challenging, especially at high resolution. Due to the lack of a commonly accepted conceptual framework in stable isotope ecology, the analysis and the graphical representation of stable isotope spatial and temporal dynamics of stable isotope value at the organism or community scale remains in the past often descriptive and qualitative, impeding the quantitative detection of relevant functional patterns. The recent Community Trajectory Analysis (CTA) framework provides more explicit perspectives for the analysis and the visualization of ecological trajectories. Building on CTA, we developed the Stable Isotope Trajectory Analysis (SITA) framework, to analyse the geometric properties of stable isotope trajectories on n-dimensional (n≥2) spaces of analysis defined analogously to the traditional multivariate spaces (Ω) used in community ecology. This approach provides new perspectives into the quantitative analysis of spatio-temporal trajectories in stable isotope spaces (Ωδ) and derived structural and functional dynamics (ΩƔ space). SITA allows the calculation of a set of trajectory metrics, based on either trajectory distances or directions, and new graphical representation solutions, both easily performable in a R environment. Here, we illustrated the use of our approach by reanalyzing previously published datasets from marine, terrestrial and freshwater ecosystems. We highlight the insights provided by this new analytic framework at the individual, population, community and ecosystems levels, and discuss applications, limitations and development potential.